How did they vote? - United Nations General Assembly Voting patterns

Multidimensional scaling

Photo by Daryan Shamkhali on Unsplash

…75 years since its founding, the General Assembly remains the global forum where all countries have a voice.
— Antonio Guterres, United Nations Secretary-General, 10 January 2021

Multidimensional scaling is a technique that aims to visualize the level of similarity among individual points in a dataset.

The resulting visual is a scatter plot where data points appear to attract and repeal each other within the same quadrant.

Ingest

This is a dataset of member states roll-call votes in the UN General Assembly from 1946 to 2020 (sessions 1 to 75).

load("archetypes/un-votes.RData")

completeVotes

Understanding the data

Resid: New roll-call vote id, number is sequential within sessions (session + 3 digit sequential number).
Member: Is country a member of the United Nations at the time of the vote?
Issue code
- ME: Votes relating to the Palestinian conflict (19%)
- NU: Votes relating to nuclear weapons and nuclear material (13%)
- DI: Votes relating to arms control and disarmament (16%)
- CO: Votes relating to colonialism (18%)
- HR: Votes relating to human rights (17%)
- EC: Votes relating to (economic) development (9%)
Vote choice
- 1 – Yes
- 2 – Abstain
- 3 – No
- 8 – Absent
- 9 – Not a member

summary <- completeVotes %>%
  count(unres, resid, year, session, abstain, yes, no, date, short, descr, me, nu, di, hr, co, ec)

summary

Wrangle

Tasks:
- Filter for an issue (topic).
- Create a digit_vote column where:
  - digit_vote = 2 if Member state voted Yes to a resolution.
  - digit_vote = 0 if Member state voted No to a resolution.
  - digit_vote = 1 otherwise (whether the Member state abstained to vote, was abscent for this vote, or was not yet a UN member).

df <- completeVotes %>% 
  filter(ec == 1, !vote == 9, nchar(Country) == 3) %>%
  select(vote, Country, Countryname, year, resid) %>%
  mutate(digit_vote = case_when(
    vote == 1 ~ 2,
    #vote == 2 ~ 1,
    vote == 3 ~ 0,
    TRUE ~ 1
  ))

Data cleanup. Delete records when more than one Member State vote for a single resolution was recorded.
Pivot dataset to create a Matrix.
Replace NA with 1.

df_1 <- df %>%
  group_by(Country, resid) %>%
  filter(n()==1)

df_2 <- df_1 %>%
  select(Country, resid, digit_vote) %>%
  pivot_wider(., names_from=resid, values_from=digit_vote) %>%
  replace(is.na(.), 1)

df_2

Time to run Multidimentional Scaling algorithm

df_3 <- df_2 %>%
  select(-`Country`)

# Compute MDS
mds <- df_3 %>%
  dist() %>%          
  cmdscale() %>%
  as_tibble()

mds

Adding kmeans clustering

This is not absolutelly necessary, but is a nice we to visually highlight groups,

# K-means clustering
clust <- kmeans(mds, 5)$cluster %>%
  as.factor()

mds <- mds %>%
  mutate(groups = clust)

df_4 <- data.frame(country = df_3$Country, 
                                     dim1 = mds$V1, 
                                     dim2 = mds$V2, 
                                     groups = mds$groups)

df_4

Final output

using ggscatter plot

# Plot and color by groups
g3 <- ggscatter(df_4, x = "dim1", y = "dim2", 
          label = df_4$country,
          color = "groups",
          palette = "jco",
          size = 1, 
          ellipse = TRUE,
          ellipse.type = "convex",
          repel = TRUE,
          interactive = TRUE)

girafe(ggobj = g3, width_svg = 13, height_svg = 7,
       options = list(opts_sizing(rescale = TRUE, width = 1.0)))

Interactive output - Comparing how Ethiopia voted in the GA

country_displayed = c("China", "France", "United States of America", 
                      "United Kingdom of Great Britain and Northern Ireland", "Russian Federation", 
                      "Israel", "iran_Iran (Islamic Republic of)", "India", "Pakistan", "Ethiopia")

df_ref <- read_csv("archetypes/CountryRefData.csv")

df_joined <- inner_join(df_4, df_ref, by=c("country" = "CountryCode")) %>%
  mutate(groups = as_factor(groups)) %>%
  mutate(point_size = if_else(country == "ETH", 50, 40)) %>%
  mutate(point_alpha = if_else(country == "ETH", 1, 1))

g1 <- ggplot(df_joined, aes(x = dim1, y = dim2)) +
  geom_point_interactive(
      aes(tooltip = paste0(CountryName,"\n", Region), 
          color = groups, size = point_size, alpha = point_alpha))+ 
  geom_text_repel(aes(label = CountryName), 
                  data = df_joined[df_joined$CountryName %in% country_displayed,], size = 10) +
  theme_tufte(base_size = 15) +
  theme(
        plot.title = element_blank(),
        axis.title.x = element_blank(),
        axis.title.y = element_blank(),
        axis.text=element_blank(),
        axis.ticks=element_blank(),
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank(),
        legend.position="none",
        plot.margin = unit(c(1, 5, 1, 1), "lines"),
  )

girafe(ggobj = g1, width_svg = 13, height_svg = 7,
       options = list(opts_sizing(rescale = TRUE, width = 1.0)))

References

citations for narrative and data sources

Data sources: Harvard Dataverse, GO